Comparison between the ages (after controlling for type and gender)

Questions

  • What are the differences between the ages?
  • Which genes and pathways are differentially expressed between 8w and 52w, between 52w and 104w, between 8w and 104w?
  • Are they the same? Is there a gradient?

Age effect - General Questions

  • What are the differences between the ages?
  • Which genes and pathways are differentially expressed between 8w and 52w, between 52w and 104w, between 8w and 104w? Are they the same? Is there a gradient?
  • Are they different for the two genders?
  • Are they different for the two types?

Loads

Libraries and functions

Warning message in is.na(x[[i]]):
“is.na() applied to non-(list or vector) of type 'environment'”Warning message in rsqlite_fetch(res@ptr, n = n):
“Don't need to call dbFetch() for statements, only for queries”
==========================================================================
*
*  Package WGCNA 1.63 loaded.
*
*    Important note: It appears that your system supports multi-threading,
*    but it is not enabled within WGCNA in R. 
*    To allow multi-threading within WGCNA with all available cores, use 
*
*          allowWGCNAThreads()
*
*    within R. Use disableWGCNAThreads() to disable threading if necessary.
*    Alternatively, set the following environment variable on your system:
*
*          ALLOW_WGCNA_THREADS=<number_of_processors>
*
*    for example 
*
*          ALLOW_WGCNA_THREADS=4
*
*    To set the environment variable in linux bash shell, type 
*
*           export ALLOW_WGCNA_THREADS=4
*
*     before running R. Other operating systems or shells will
*     have a similar command to achieve the same aim.
*
==========================================================================


Allowing multi-threading with up to 4 threads.
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."

Data

Stats

Wald padj < 0.05LFC > 0 (Wald padj < 0.05)LFC < 0 (Wald padj < 0.05)
52w VS 8w21571176 981
104w VS 52w228412521032
104w VS 8w319217011491

Differentially expressed genes

  52w VS 8w 104w VS 52w  104w VS 8w 
  0.3036625   0.2570053   0.3956767 

Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in stack.default(getgo(rownames(l$deg), "mm10", "geneSymbol")):
“non-vector elements will be ignored”Warning message in stack.default(getgo(rownames(as.data.frame(l$deg)), "mm10", "geneSymbol", :
“non-vector elements will be ignored”

Counts

Genes that are differentially expressed in 52W vs 8W with abs(FC) > 2

Genes that are differentially expressed in 104W vs 8W with abs(FC) > 2

Genes that are differentially expressed in 104W vs 52W with abs(FC) > 2

Compare the numbers

Differentially expressed genes

Differentially more expressed genes

Differentially less expressed genes

Compare the intersections in term of expressions

Genes that are differentially expressed in 52W vs 8W and in 104W vs 8W (and not in 104w vs 52w)

52w > 8w & 104w > 8w52w > 8w & 104w < 8w52w < 8w & 104w > 8w52w < 8w & 104w < 8w
0 1072750

With abs(FC) > 1

Genes that are differentially expressed in 52W vs 8W and in 104W vs 52W (and not in 104w vs 52w)

52w > 8w & 104w > 52w52w > 8w & 104w < 52w52w < 8w & 104w > 52w52w < 8w & 104w < 52w
5630 0 329

With abs(FC) > 1

Genes that are differentially expressed in 104W vs 8W and in 104W vs 52W (and not in 52w vs 8w)

104w > 8w & 104w > 52w104w > 8w & 104w < 52w104w < 8w & 104w > 52w104w < 8w & 104w < 52w
5040 0 567

With abs(FC) > 1

Genes that are differentially expressed in 52W vs 8W, in 104W vs 8W and in 104W vs 52W

52w > 8w & 104w > 8w & 104w > 52w52w > 8w & 104w > 8w & 104w < 52w52w > 8w & 104w < 8w & 104w > 52w52w > 8w & 104w < 8w & 104w < 52w52w < 8w & 104w > 8w & 104w > 52w52w < 8w & 104w > 8w & 104w < 52w52w < 8w & 104w < 8w & 104w > 52w52w < 8w & 104w < 8w & 104w < 52w
1890 6 7 15 6 0 46

With abs(FC) > 1

DEG into gene co-expression network

  • White: up-regulated
  • Black: down-regulated

Order: 52w VS 8w, 104w VS 52w, 104w VS 8w

GO analysis

Biological process

Dot-plot with the most over-represented GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

Network based on description similarity

52w VS 8w

<!DOCTYPE html>

104w VS 52w

<!DOCTYPE html>

104w VS 8w

<!DOCTYPE html>

Cellular components

Dot-plot with the most over-represented GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

Molecular functions

Dot-plot with the most over-represented GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

KEGG pathways

[1] "Note: 4 of 4750 unique input IDs unmapped."
[1] "Note: 4 of 4750 unique input IDs unmapped."
[1] "Note: 4 of 4750 unique input IDs unmapped."
[1] "Note: 4 of 4750 unique input IDs unmapped."
[1] "Note: 4 of 4750 unique input IDs unmapped."
[1] "Note: 4 of 4750 unique input IDs unmapped."
[1] "Note: 4 of 4750 unique input IDs unmapped."
[1] "Note: 4 of 4750 unique input IDs unmapped."
[1] "Note: 4 of 4750 unique input IDs unmapped."
[1] "Note: 4 of 4750 unique input IDs unmapped."
[1] "Note: 4 of 4750 unique input IDs unmapped."
[1] "Note: 4 of 4750 unique input IDs unmapped."
[1] "Note: 4 of 4750 unique input IDs unmapped."
[1] "Note: 4 of 4750 unique input IDs unmapped."
[1] "Note: 4 of 4750 unique input IDs unmapped."
[1] "Note: 4 of 4750 unique input IDs unmapped."

Pathway graphs available at ../results/dge/age-effect/age/over_repr_kegg/

[1] "Note: 4 of 4750 unique input IDs unmapped."
[1] "Note: 4 of 4750 unique input IDs unmapped."

Pathway graphs available at ../results/dge/age-effect/age/under_repr_kegg/

Warning message in stack.default(getgo(rownames(age_deg$deg), "mm10", "geneSymbol", :
“non-vector elements will be ignored”
valuesind
00650AACS
00970AARS
02010ABCA2
04142ABCA2
02010ABCA5
02010ABCA6
$`52w VS 8w`
  1. '05322'
  2. '05323'
  3. '04744'
  4. '04672'
  5. '04110'
  6. '04060'
  7. '04115'
  8. '05020'
  9. '04114'
$`104w VS 52w`
  1. '05322'
  2. '05323'
  3. '04744'
  4. '04672'
  5. '04110'
  6. '04060'
  7. '04115'
$`104w VS 8w`
  1. '05322'
  2. '05323'
  3. '04744'
  4. '04672'
  5. '04110'
  6. '04060'
  7. '04115'
  8. '05020'
  9. '04114'
  10. '05310'
  11. '05150'
  12. '04514'
  13. '05140'
  14. '04914'
  15. '04620'
  16. '05145'
valuesind
39104744 ARRB1
68404744 CALM1
69904744 CALM2
71404744 CALM3
177604744 GNAT1
178004744 GNB1
186504744 GUCA1B
334604744 PDE6A
334804744 PDE6B
335004744 PDE6G
432704744 RGS9
432804744 RHO
454704744 SLC24A1
 
Error in extract_go_de_genes(over_represented_KEGG[[x]], x, de_genes, : could not find function "extract_go_de_genes"
Traceback: